Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "28" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 12.678511 | 17.208576 | 11.459459 | 12.445742 | 9.230687 | 10.530177 | 0.777621 | 3.362556 | 0.0259 | 0.0256 | 0.0013 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 88.00% | 0.00% | - | - | 9.396087 | 25.965518 | 1.107314 | 4.280740 | 5.438185 | 4.552302 | 2.223902 | 18.292496 | 0.3390 | 0.1521 | 0.2425 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 75.84% | 0.00% | - | - | 10.918486 | 31.467380 | 0.467310 | 4.412635 | 4.800927 | 4.120470 | 2.110180 | 9.402184 | 0.3869 | 0.1702 | 0.2764 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 82.72% | 0.00% | - | - | 8.134090 | 23.927734 | 0.428379 | 3.677396 | 4.665250 | 3.813288 | 4.189043 | 17.859481 | 0.3532 | 0.1582 | 0.2510 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 96.49% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0966 | 0.0554 | 0.0524 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 87.40% | 0.00% | - | - | 8.596694 | 22.532447 | 0.600064 | 3.087388 | 4.359284 | 7.078967 | 5.557629 | 18.797093 | 0.3529 | 0.1496 | 0.2595 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 86.22% | 0.00% | - | - | 9.909585 | 25.939192 | 1.668397 | 4.299828 | 6.271291 | 5.246005 | 2.965014 | 15.467416 | 0.3547 | 0.1554 | 0.2608 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 82.86% | 0.00% | - | - | 10.435302 | 26.672481 | 0.801364 | 3.614964 | 3.225960 | 6.322074 | 3.962432 | 19.591515 | 0.3563 | 0.1581 | 0.2591 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 87.04% | 0.00% | - | - | 10.208322 | 26.683416 | 0.459083 | 3.388878 | 3.793535 | 6.459729 | 2.954766 | 15.132206 | 0.3544 | 0.1518 | 0.2610 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 97.48% | 0.00% | - | - | 12.600912 | 26.965797 | 0.366142 | 2.953770 | 5.468216 | 9.505774 | 4.034549 | 16.403026 | 0.2924 | 0.1149 | 0.2273 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 87.61% | 0.00% | - | - | 12.576202 | 29.328071 | 0.023514 | 3.056162 | 5.043936 | 7.780274 | 3.893547 | 19.646332 | 0.3598 | 0.1510 | 0.2683 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 85.15% | 0.00% | - | - | 9.998757 | 23.779769 | -0.027140 | 2.864351 | 5.559347 | 8.352924 | 4.328713 | 19.678975 | 0.3581 | 0.1520 | 0.2639 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 87.63% | 0.00% | - | - | 10.497299 | 25.313829 | -0.140595 | 2.770894 | 5.478759 | 6.313298 | 4.220883 | 12.698445 | 0.3546 | 0.1503 | 0.2622 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 88.11% | 0.00% | - | - | 11.968388 | 29.345292 | -0.164261 | 2.871118 | 7.317595 | 10.130592 | 3.892634 | 14.023781 | 0.3507 | 0.1462 | 0.2604 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 85.85% | 0.00% | - | - | 9.750689 | 25.066502 | -0.080547 | 3.135188 | 5.132030 | 5.545451 | 3.332426 | 22.858606 | 0.3679 | 0.1581 | 0.2685 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 73.27% | 0.00% | - | - | 11.680612 | 30.028822 | -0.177756 | 3.307160 | 6.816145 | 8.714662 | 3.946246 | 18.339004 | 0.3918 | 0.1726 | 0.2817 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.54% | 88.00% | 0.00% | - | - | 11.696300 | 28.686807 | -0.173552 | 3.127210 | 8.268498 | 5.108652 | 6.017452 | 25.444999 | 0.3604 | 0.1502 | 0.2665 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 83.61% | 0.00% | - | - | 10.154441 | 27.285524 | -0.425236 | 4.015087 | 9.298397 | 16.900102 | 0.872628 | 18.392528 | 0.3818 | 0.1680 | 0.2701 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 78.01% | 0.00% | - | - | 10.447418 | 27.337157 | -0.013304 | 2.797882 | 6.388491 | 9.998958 | 3.320621 | 22.269075 | 0.3961 | 0.1765 | 0.2837 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.00% | 61.53% | 0.00% | - | - | 13.423753 | 29.788038 | 0.592585 | 2.487398 | 3.417504 | 5.924934 | 0.655219 | 5.460869 | 0.4461 | 0.2083 | 0.3095 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 87.36% | 0.00% | - | - | 9.957270 | 24.078414 | -0.196390 | 2.724401 | 7.104656 | 10.807942 | 3.722259 | 25.851658 | 0.3618 | 0.1547 | 0.2656 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 70.56% | 0.00% | - | - | 9.880410 | 22.522854 | -0.203599 | 2.619662 | 6.492803 | 8.198901 | 2.422759 | 8.446044 | 0.4186 | 0.1894 | 0.2939 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 89.02% | 0.00% | - | - | 10.507884 | 24.774204 | -0.375265 | 2.309288 | 6.111122 | 8.303455 | 4.806075 | 19.073640 | 0.3553 | 0.1476 | 0.2685 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 88.27% | 0.00% | - | - | 10.857826 | 25.340935 | -0.382153 | 2.413305 | 6.187759 | 10.277611 | 5.333438 | 21.252147 | 0.3516 | 0.1508 | 0.2649 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 93.28% | 0.00% | - | - | 10.060177 | 24.361294 | -0.333844 | 2.646987 | 12.791279 | 9.003764 | 4.831863 | 20.515717 | 0.3375 | 0.1487 | 0.2409 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 88.81% | 0.00% | - | - | 10.750348 | 25.085795 | -0.283536 | 2.525676 | 5.526803 | 9.185594 | 3.913141 | 16.412545 | 0.3567 | 0.1521 | 0.2697 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 17.208576 | 12.678511 | 17.208576 | 11.459459 | 12.445742 | 9.230687 | 10.530177 | 0.777621 | 3.362556 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.965518 | 9.396087 | 25.965518 | 1.107314 | 4.280740 | 5.438185 | 4.552302 | 2.223902 | 18.292496 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 31.467380 | 31.467380 | 10.918486 | 4.412635 | 0.467310 | 4.120470 | 4.800927 | 9.402184 | 2.110180 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 23.927734 | 8.134090 | 23.927734 | 0.428379 | 3.677396 | 4.665250 | 3.813288 | 4.189043 | 17.859481 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 22.532447 | 8.596694 | 22.532447 | 0.600064 | 3.087388 | 4.359284 | 7.078967 | 5.557629 | 18.797093 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.939192 | 9.909585 | 25.939192 | 1.668397 | 4.299828 | 6.271291 | 5.246005 | 2.965014 | 15.467416 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 26.672481 | 10.435302 | 26.672481 | 0.801364 | 3.614964 | 3.225960 | 6.322074 | 3.962432 | 19.591515 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 26.683416 | 10.208322 | 26.683416 | 0.459083 | 3.388878 | 3.793535 | 6.459729 | 2.954766 | 15.132206 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 26.965797 | 12.600912 | 26.965797 | 0.366142 | 2.953770 | 5.468216 | 9.505774 | 4.034549 | 16.403026 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 29.328071 | 12.576202 | 29.328071 | 0.023514 | 3.056162 | 5.043936 | 7.780274 | 3.893547 | 19.646332 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 23.779769 | 23.779769 | 9.998757 | 2.864351 | -0.027140 | 8.352924 | 5.559347 | 19.678975 | 4.328713 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.313829 | 25.313829 | 10.497299 | 2.770894 | -0.140595 | 6.313298 | 5.478759 | 12.698445 | 4.220883 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 29.345292 | 29.345292 | 11.968388 | 2.871118 | -0.164261 | 10.130592 | 7.317595 | 14.023781 | 3.892634 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.066502 | 9.750689 | 25.066502 | -0.080547 | 3.135188 | 5.132030 | 5.545451 | 3.332426 | 22.858606 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 30.028822 | 30.028822 | 11.680612 | 3.307160 | -0.177756 | 8.714662 | 6.816145 | 18.339004 | 3.946246 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 28.686807 | 28.686807 | 11.696300 | 3.127210 | -0.173552 | 5.108652 | 8.268498 | 25.444999 | 6.017452 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 27.285524 | 10.154441 | 27.285524 | -0.425236 | 4.015087 | 9.298397 | 16.900102 | 0.872628 | 18.392528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 27.337157 | 10.447418 | 27.337157 | -0.013304 | 2.797882 | 6.388491 | 9.998958 | 3.320621 | 22.269075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 29.788038 | 13.423753 | 29.788038 | 0.592585 | 2.487398 | 3.417504 | 5.924934 | 0.655219 | 5.460869 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Temporal Discontinuties | 25.851658 | 24.078414 | 9.957270 | 2.724401 | -0.196390 | 10.807942 | 7.104656 | 25.851658 | 3.722259 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 22.522854 | 22.522854 | 9.880410 | 2.619662 | -0.203599 | 8.198901 | 6.492803 | 8.446044 | 2.422759 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 24.774204 | 10.507884 | 24.774204 | -0.375265 | 2.309288 | 6.111122 | 8.303455 | 4.806075 | 19.073640 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.340935 | 25.340935 | 10.857826 | 2.413305 | -0.382153 | 10.277611 | 6.187759 | 21.252147 | 5.333438 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 24.361294 | 10.060177 | 24.361294 | -0.333844 | 2.646987 | 12.791279 | 9.003764 | 4.831863 | 20.515717 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | N01 | RF_maintenance | nn Shape | 25.085795 | 25.085795 | 10.750348 | 2.525676 | -0.283536 | 9.185594 | 5.526803 | 16.412545 | 3.913141 |